Examining nonadherence in the treatment of tuberculosis: The patterns that lead to failure.

AIMS: Adherence has been shown to be a major predictor of tuberculosis treatment failure and relapse. The current adherence metrics can be improved to provide higher resolution of adherence patterns and identify patients in need of alternative treatment interventions. We investigated how adherence patterns affect treatment outcomes, when adherence is likely to decrease during treatment and which patients are at risk of being nonadherent. METHODS: Individual-level data were pooled from 3 clinical trials (n = 3724) for treatment of drug susceptible tuberculosis where monthly or weekly adherence patterns were collected and adherence patterns were quantified to assess the impact of clustered missed doses vs. randomly missed doses on tuberculosis treatment outcomes. Significance was determined through univariate and multivariate cox regression models. RESULTS: Patients who miss doses in clusters have an increased hazard risk for unfavourable outcomes and missing as little as 4 treatment days in 1 month resulted in 61% higher risk of unfavourable outcomes compared to patients who missed no treatment days (P < .01). Patients older than 50 years, and patients who experienced an adverse event were associated with lower adherence. CONCLUSION: Our results show that the pattern in which patients miss their drugs is important to their overall outcomes and missing treatment days in clusters rather than randomly increases the risk of poor outcomes. In the future more intensive and longitudinal adherence measurements will be valuable for clinical trials and regimen design and interpretation.

Mathematical model and tool to explore shorter multi-drug therapy options for active pulmonary tuberculosis.

Standard treatment for active tuberculosis (TB) requires drug treatment with at least four drugs over six months. Shorter-duration therapy would mean less need for strict adherence, and reduced risk of bacterial resistance. A system pharmacology model of TB infection, and drug therapy was developed and used to simulate the outcome of different drug therapy scenarios. The model incorporated human immune response, granuloma lesions, multi-drug antimicrobial chemotherapy, and bacterial resistance. A dynamic population pharmacokinetic/pharmacodynamic (PK/PD) simulation model including rifampin, isoniazid, pyrazinamide, and ethambutol was developed and parameters aligned with previous experimental data. Population therapy outcomes for simulations were found to be generally consistent with summary results from previous clinical trials, for a range of drug dose and duration scenarios. An online tool developed from this model is released as open source software. The TB simulation tool could support analysis of new therapy options, novel drug types, and combinations, incorporating factors such as patient adherence behavior.

Tuberculosis drugs' distribution and emergence of resistance in patient's lung lesions: A mechanistic model and tool for regimen and dose optimization.

BACKGROUND: The sites of mycobacterial infection in the lungs of tuberculosis (TB) patients have complex structures and poor vascularization, which obstructs drug distribution to these hard-to-reach and hard-to-treat disease sites, further leading to suboptimal drug concentrations, resulting in compromised TB treatment response and resistance development. Quantifying lesion-specific drug uptake and pharmacokinetics (PKs) in TB patients is necessary to optimize treatment regimens at all infection sites, to identify patients at risk, to improve existing regimens, and to advance development of novel regimens. Using drug-level data in plasma and from 9 distinct pulmonary lesion types (vascular, avascular, and mixed) obtained from 15 hard-to-treat TB patients who failed TB treatments and therefore underwent lung resection surgery, we quantified the distribution and the penetration of 7 major TB drugs at these sites, and we provide novel tools for treatment optimization. METHODS AND FINDINGS: A total of 329 plasma- and 1,362 tissue-specific drug concentrations from 9 distinct lung lesion types were obtained according to optimal PK sampling schema from 15 patients (10 men, 5 women, aged 23 to 58) undergoing lung resection surgery (clinical study NCT00816426 performed in South Korea between 9 June 2010 and 24 June 2014). Seven major TB drugs (rifampin [RIF], isoniazid [INH], linezolid [LZD], moxifloxacin [MFX], clofazimine [CFZ], pyrazinamide [PZA], and kanamycin [KAN]) were quantified. We developed and evaluated a site-of-action mechanistic PK model using nonlinear mixed effects methodology. We quantified population- and patient-specific lesion/plasma ratios (RPLs), dynamics, and variability of drug uptake into each lesion for each drug. CFZ and MFX had higher drug exposures in lesions compared to plasma (median RPL 2.37, range across lesions 1.26-22.03); RIF, PZA, and LZD showed moderate yet suboptimal lesion penetration (median RPL 0.61, range 0.21-2.4), while INH and KAN showed poor tissue penetration (median RPL 0.4, range 0.03-0.73). Stochastic PK/pharmacodynamic (PD) simulations were carried out to evaluate current regimen combinations and dosing guidelines in distinct patient strata. Patients receiving standard doses of RIF and INH, who are of the lower range of exposure distribution, spent substantial periods (>12 h/d) below effective concentrations in hard-to-treat lesions, such as caseous lesions and cavities. Standard doses of INH (300 mg) and KAN (1,000 mg) did not reach therapeutic thresholds in most lesions for a majority of the population. Drugs and doses that did reach target exposure in most subjects include 400 mg MFX and 100 mg CFZ. Patients with cavitary lesions, irrespective of drug choice, have an increased likelihood of subtherapeutic concentrations, leading to a higher risk of resistance acquisition while on treatment. A limitation of this study was the small sample size of 15 patients, performed in a unique study population of TB patients who failed treatment and underwent lung resection surgery. These results still need further exploration and validation in larger and more diverse cohorts. CONCLUSIONS: Our results suggest that the ability to reach and maintain therapeutic concentrations is both lesion and drug specific, indicating that stratifying patients based on disease extent, lesion types, and individual drug-susceptibility profiles may eventually be useful for guiding the selection of patient-tailored drug regimens and may lead to improved TB treatment outcomes. We provide a web-based tool to further explore this model and results at http://saviclab.org/tb-lesion/.